Identifying and managing AUD risk in acute care

AT OUR COMMUNITY HOSPITAL in northern New Jersey, nurses observed an apparent increase in patients with symptoms of alcohol withdrawal without a documented alcohol use disorder (AUD) diagnosis. The American Psychiatric Association describes AUD as a maladaptive pattern of alcohol consumption that results in “clinically significant impairment” or “psychological distress.”

During intake screenings, many of these patients denied typical risk factors, such as family history or prior alcohol-related health issues. Nursing staff suspected missed opportunities for early detection and intervention.

A team of nurses—including a bedside nurse, a director, and a doctorally prepared nurse—launched a retrospective chart review to inform practice and to better understand changes in AUD prevalence and screening accuracy before and after the COVID-19 pandemic. Although a retrospective review has limitations and may not uncover specific behaviors in this population, such as stigma-related attitudes, the team wanted to determine if prevalence had increased and if misdocumentation played a role in missed cases of AUD.

The pandemic and AUD

A study by Capasso and colleagues documented an increase in alcohol consumption related to the COVID-19 pandemic in a hospital setting with a primarily white population. Sharma and colleagues also identified an increase in alcohol withdrawal in hospitalized patients during the pandemic.

A study by Pollard and colleagues found a 41% increase in heavy drinking among women during the COVID-19 pandemic. In addition, according to Patrick and colleagues, binge drinking among adults ages 35 to 50 reached 29% in 2022, 3.5% higher than the previous year.

These findings highlight the growing importance of early identification and management of alcohol withdrawal in acute care settings. Studies, such as that by Melkonian and colleagues, have shown that patients who deteriorate due to alcohol withdrawal while hospitalized have a longer length of stay (LOS) and a higher risk of mortality. To combat these outcomes, Melkonian’s research team used the Clinical Institute Withdrawal Assessment (CIWA) scale, which helped contribute to decreased LOS, benzodiazepine use, and need for ICU consultation or Rapid Response calls.

A study conducted by Hyde and team found that, compared to other healthcare professionals, nurses reported the lowest satisfaction and motivation scores regarding care of patients with AUD. This finding might point to potential stigma against patients with AUD, suggesting that nurse attitudes and experiences also may play a role in how these patients are identified and cared for.

Our retrospective review project

Rather than introducing new theory or challenging existing literature, we used local findings to reinforce and apply what nursing research already supports. We conducted this Institutional Review Board-approved, descriptive correlational chart review using electronic health record (EHR) data from two time periods: March 1, 2019–February 28, 2020 (pre-pandemic) and March 1, 2022–February 28, 2023 (post-pandemic). Following the Centers for Disease Control and Prevention timeline for the pandemic, we excluded the pandemic years from our collected data to avoid confounding effects related to COVID-19, which significantly disrupted healthcare delivery and patient patterns in New Jersey from March 2020 through early 2022.

Inclusion criteria focused on adults over age 18 admitted to the hospital as inpatients or observation patients, who had a documented CIWA for Alcohol, Revised (CIWA-Ar) score with a discharge diagnosis of AUD. Importantly, this approach may exclude patients who aren’t screened or formally diagnosed, representing missed opportunities for identification and care. As a result, the project may not fully capture all patients at risk for alcohol withdrawal. The team excluded patients readmitted to the hospital within 30 days.

Data included demographic characteristics (age, race, gender, ethnicity, admission diagnoses, employment and marital status) and scores from the Prediction of Alcohol Withdrawal Severity Scale (PAWSS) and CIWA-Ar. Descriptive and correlational analyses explored relationships between demographic factors and the severity of AUD withdrawal. (See About the tools.)

The data confirmed that AUD cases increased after the COVID-19 pandemic and that post-pandemic demographic predictors—such as age and employment status—have weakened as reliable risk markers, a shift consistent with national trends reported by Patrick and colleagues. In response, we drew on established, validated PAWSS and CIWA-Ar tools and evidence-based guidelines from the American Society of Addiction Medicine (ASAM) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) to argue that nurses move away from selective, demographic-based screening toward universal, unbiased assessment practices. Ultimately, we used this information to close the gap between what the evidence recommends and bedside practice.

Findings and outcomes

The number of patients with AUD as a discharge diagnosis in the periods reviewed increased by 14%, from 233 pre-pandemic to 266 post-pandemic. The Pearson’s chi-square test revealed that the proportion of patients with AUD on discharge increased from 2.9% to 3.5% of total hospital discharges (p=0.03). This includes all patients with AUD and may include patients with symptoms of withdrawal from binge drinking. Demographic characteristics (age, gender, race) were similar across both periods. Most patients were white males, with an average age of 55 years.

The observed increase in hospital-based AUD diagnoses in our project may reflect a growing concentration of higher-acuity alcohol-related illness rather than a simple increase in prevalence. In other words, the hospital is increasingly encountering patients with more severe manifestations of alcohol use, consistent with national evidence of sustained high rates of binge drinking, as documented by Patrick and colleagues. For clinical practice, this underscores the importance of robust screening, early identification, and standardized management of alcohol withdrawal and AUD in inpatient settings. It also reinforces the need for consistent use of validated tools, such as PAWSS and CIWA-Ar, to guide risk stratification and treatment. (See Score distribution.)

Notably, the data revealed inconsistencies in PAWSS documentation, with approximately 20% of pre-pandemic and 18% of post-pandemic patients having scores of zero or missing data. Targeted chart audits found that most missing or zero scores reflected clinically appropriate omissions, such as a patient with a prior diagnosis of AUD or a patient receiving comfort care. However, some omissions resulted from incomplete documentation or patient-reported misinformation.

These gaps have important implications for both data integrity and clinical care. From a data-quality perspective, missing or zero PAWSS scores may lead to misclassification of withdrawal risk and limit the accuracy of trend analysis. Clinically, inconsistent screening introduces the potential for under-recognition of patients at risk for severe alcohol withdrawal, which may delay treatment and contribute to preventable complications, highlighting the need for more standardized and reliable assessment practices. (See Missing data.)

Across both periods, higher CIWA-Ar scores were associated with longer hospital stays and documented AUD diagnoses at discharge. Spearman correlations revealed significant associations. Before the pandemic, higher CIWA-Ar scores were associated with increased LOS (r= .169, p<.01) and a greater likelihood of an AUD diagnosis on discharge (r=.169, p<.01). Similar but slightly stronger associations were observed after the pandemic, with higher CIWA-Ar scores associated with longer LOS (r= .171, p<.01) and AUD diagnosis on discharge (r=.200, p<.01).

These observed correlations suggest that CIWA‑Ar scores are clinically meaningful indicators associated with important patient outcomes. Consistent CIWA‑Ar use may support earlier recognition of withdrawal severity, more standardized care, and improved risk stratification, while inconsistent use could contribute to misclassification of withdrawal risk and variability in care delivery.

Although these associations persisted, demographic variables such as age and employment status (employed vs. unemployed) demonstrated weaker relationships with alcohol withdrawal severity and CIWA-Ar scores in the post-pandemic period. Pre-pandemic CIWA-Ar scores showed a small but significant inverse association with age (r= -.14, p=.03) and a small positive relationship with employment (r= .14, p= .03). In other words, older age was associated with slightly lower CIWA-Ar scores.

Although these differences aren’t statistically significant, unemployed patients demonstrated higher mean CIWA-Ar scores than employed patients both pre- and post-pandemic. This pattern may align with prior observations by Ramos and colleagues, who found that patients treated in the ICU for severe alcohol withdrawal symptoms were more likely to be younger, white, and unemployed compared with patients managed on medical units. (See Spearman correlation matrix: Pre-pandemic)

Post‑pandemic CIWA‑Ar scores demonstrated weakened or nonsignificant associations with demographic factors such as age and employment status (employed vs unemployed), suggesting that alcohol withdrawal severity may be less strongly linked to these characteristics than in the pre-pandemic periods. In contrast, CIWA‑Ar scores remained significantly associated with LOS and AUD diagnosis, reinforcing their clinical relevance as indicators of meaningful patient outcomes.

These findings suggest that demographic characteristics alone may be limited in identifying patients at risk for alcohol withdrawal in the post-pandemic inpatient setting. Nursing assessment may therefore benefit from consistent, universal screening using validated tools such as PAWSS and CIWA‑Ar rather than relying primarily on selective screening based on demographic risk profiles. This approach aligns with observed patterns in which clinical screening measures demonstrate stronger and more consistent associations with withdrawal severity and related outcomes than demographic variables. (See Spearman correlation matrix: Post-pandemic)

Nursing implications

The increase in patients with AUD after the pandemic underscores the need for heightened nursing awareness and consistent use of validated screening tools, such as the CIWA-Ar and PAWSS. Hyde and colleagues discuss the need for tailored educational interventions to reduce stigma associated with AUD and improve documentation.

Reducing stigma

Stigma associated with AUD and nurses’ preconceptions about patients with the condition can result in complications and poor quality care. According to the NIAAA, the stigma of AUD can leave patients feeling ashamed and in denial about their condition, ultimately preventing them from seeking treatment or disclosing information to healthcare professionals. The NIAAA also noted that this stigma may lead some clinicians to shorten visit times and show little empathy for the patient.

Motivational interviewing techniques and person-first language can help break down barriers for clinicians and patients. Emery and Wimer describe motivational interviewing as healthcare professionals and patients working together to achieve a therapeutic outcome. According to the National Institutes of Health, person-first language emphasizes the person rather than their disorder, disease, condition, or disability. For example, rather than saying “alcoholic patient,” say, “a person with alcohol use disorder.”

Screening and assessment best practices

As described in the National Teaching Institute Research Abstracts, nurse-led early detection and intervention using validated tools can decrease the risk of deterioration and improve patient outcomes. Pribék and colleagues noted that the CIWA-Ar is a well-validated and widely used tool for assessment and monitoring of alcohol withdrawal severity in clinical settings. A study conducted by Melkonian and colleagues found that consistent use of the CIWA-Ar scale can improve patient outcomes, including decreased average LOS, and enhance patient safety by reducing clinical deterioration. The PAWSS tool, according to Gottlieb and colleagues, is a validated screening tool for identifying patients at high risk for alcohol withdrawal and supporting patient outcomes.

Although the PAWSS tool effectively predicts risk for severe withdrawal, its accuracy depends on thorough physical and history assessments as well as reliable and accurate patient responses. For example, knowledge of the definition of a standard drink (12 oz. of beer, 8 to 9 oz. of craft beer or malt liquor, 5 oz. of wine, 1.5 oz of hard alcohol) can aid in ensuring an accurate assessment.

A PAWSS score of 4 or higher indicates a patient at high risk for alcohol withdrawal, prompting nurses to recommend that the provider place orders for more frequent monitoring, which typically involves using the CIWA-Ar to assess for signs of alcohol withdrawal (ranging from mild to severe). The nurse notes the patient’s heart rate and blood pressure and then asks the patient about (or the nurse observes) each symptom, rating them on a scale from 0 (no symptom) to 8 (severe symptom). Scores from 0 to 10 indicate mild alcohol withdrawal; 11 to 17, moderate; any score over 18 is considered severe. Depending on scores, the tool prompts the nurse to take a specific action regarding medication and how soon to reassess using the CIWA-Ar scale.

To promote good health outcomes in patients diagnosed with AUD in the hospital, care should continue in the post-acute care setting with screening, brief intervention, and referral to treatment. ASAM guidelines recommend continuing AUD treatment services in the outpatient setting. For those receiving pharmacotherapy, such as benzodiazepines, nurses can provide education to ensure the patient understands their ongoing treatment plan.

Accurate documentation

In our audit process, some PAWSS screenings completed by nurses lacked answers to all questions. A thorough chart review of nurse, physician, and social worker notes clarified what occurred during patient care and how alcohol withdrawal was detected. The audit identified the need for nurses to maintain vigilance when screening for AUD, even if scores appear low or when patients deny alcohol use. Clinical intuition and demographic assumptions are not substitutes for validated screening tools.

According to Maldonado and colleagues, using the EHR during patient interviews and physical assessments can increase PAWSS score accuracy, especially when patients aren’t forthcoming. When reviewing the EHR for this project, several patients had false-negative PAWSS scores as a result of misleading reports; previous history or assistance from family helped establish a true score. This finding highlights the need for nurses to systematically cross-reference patient self-reports with other sources, including family statements, EHR history, pharmacy data, and social work records to reduce missed opportunities for AUD detection.

Targeted strategies aimed at improving nurses’ skills and competence can help prevent delays in care and reduce missed cases of AUD-related withdrawal. These strategies include annual CIWA-Ar and PAWSS competency validation, unit-wide reviews of missed withdrawal cases, and simulation-based training focused on early identification and escalation.

Practice recommendations

Montalva describes steps that can help elicit reliable information from patients about their alcohol consumption, including knowledge of standard drink definitions to identify risk, using therapeutic and empathetic language, and implementing evidence-based screening tools. Our hospital integrated PAWSS into the EHR standard nursing admission assessment screening. If a patient’s condition prevents screening completion during the admission process, the inpatient nurse finishes it on the unit or when family is present to assist.

ASAM offers evidence-based guidelines for inpatient care of those with AUD. The guidelines include monitoring and supportive care. (See ASAM guidelines.)

Adjusting to a shift

Since the COVID-19 pandemic, instances of AUD in the hospital setting have increased. Pre-pandemic trends suggested a close association between AUD and age and employment status; post-pandemic patterns demonstrate a weaker correlation with these demographic factors. For this reason, diagnosing post-pandemic AUD in hospital settings requires a shift toward universal, unbiased, and rigorously documented screening.

Since the COVID-19 pandemic, demographic associations with AUD and alcohol withdrawal severity appear to have weakened, with post-pandemic data demonstrating a reduced correlation between AUD-related measures and factors such as age and employment status. These findings support the continued use of standardized, systemic screening approaches to ensure consistent identification of patients with alcohol-related risk in the inpatient setting. We suggest that future research focus on education for nurses that reinforces using validated screening tools.

Given this shift, nurses can use validated assessment tools such as the PAWSS and CIWA-Ar. In addition, nurses can provide supportive, nonjudgmental care to reduce the stigma associated with AUD, which can hinder honest disclosure, delay appropriate treatment, and negatively impact patient outcomes.

Rachel Moutis is an RN at Chilton Medical Center/Atlantic Health in Pompton Plains, New Jersey. Laura Reilly is a nursing director at Chilton Medical Center/Atlantic Health. Yvonne Wesley is an independent health consultant at Y. Wesley Consulting LLC in Delaware.

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Key words: alcohol use disorder, screening tools, alcohol withdrawal